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Recent technological advances in facial capture have made it possible to acquire high-fidelity 3D facial performance data with stunningly high spatial-temporal resolution. This paper introduces a novel facial expression transfer and editing technique for high-fidelity facial performance data. The key idea of our approach is to decompose high-fidelity facial performances into high-level facial feature lines, large-scale facial deformation and fine-scale motion details and transfer them appropriately to reconstruct the retargeted facial animation in an efficient optimization framework. This project was among Microsoft Research's contributions to SIGGRAPH 2014.

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